1. Technical Field
The present disclosure relates to medical images and, more specifically, to a system and method for automatic detection of internal structures in medical images.
2. Discussion of the Related Art
Modern radiological medical imaging devices such as multi-slice computerized tomography (CT) scanners, magnetic resonance imaging (MRI) scanners, medical ultrasonography scanners, positron emission tomography (PET) scanners and the like may be used to quickly and easily generate detailed images of a subject's body. Due to the availability of such imaging devices and their usefulness, medical imaging has become an important part of patient care.
While medical imaging scanners may be able to generate a large amount of images, a healthcare professional, such as a radiologist, must carefully consider each image in order to render a diagnosis. With the increased reliance on medical imagery and increased emphasis on the control of medical expenses, methods for computer aided diagnosis (CAD) of medical images have been developed. Approaches to CAD have focused on processing medical images to determine regions of suspicion. Radiologists may then be able to pay particular attention to the regions of suspicion and thus increase diagnostic accuracy while reducing the time required to form the diagnosis.
In order to perform CAD, it is first necessary to identify the various internal structures that are present in the image. In this step, regions or objects of interest are identified and characterized. This step is known as segmentation. Because medical images may vary widely from one another in terms of pixel density and image scope, it may be difficult for a computer to perform automatic segmentation on a medical image. Accordingly, many approaches to segmentation are semi-automatic. In semi-automatic segmentation, user input is required before the computer may accomplish segmentation.
In some forms of semi-automatic segmentation, a user must manually identify an internal structure of interest and pass this information onto the computer. For example, the user may input a seed point indicating a location that is within the internal structure of interest. The user may also identify the type of internal structure that is of interest. The computer may then use this seed point and structure identity to identify the bounds of the internal structure of interest.
Approaches to semi-automatic segmentation often vary according to the identity of the structure of interest. For example, segmentation of the lungs may be performed differently than segmentation of the colon. For this reason, different tools are often used to perform segmentation on different internal structures, and thus conventional segmentation tools often lack the versatility to perform segmentation on more than one internal structure.
Moreover, existing segmentation tools may be highly susceptible to imaging artifacts such as noise, motion and partial volume. Such artifacts may prevent accurate segmentation. Segmentation tools that are robust enough to handle one form of artifact are often susceptible to another form of artifact. For example, a tool that is robust against noise may be particularly susceptible to volume effects.
For these reasons, segmentation tools that are general enough to perform segmentation on a wide range of medical images and internal structures tend to provide unacceptable results. Other segmentation tools may provide acceptable results but may only be used for a very specialized set of medical images and internal structures. Accordingly, many varying approaches to segmentation may be performed in sequence to provide acceptable results. This practice can be complicated and time consuming.